Assessment of Quality of Experience (QoE) of Image Compression in Social Cloud Computing
dc.contributor.author | Asif Ali, L | |
dc.contributor.author | He, H | |
dc.contributor.author | Khan, Asiya | |
dc.contributor.author | Shafiq, M | |
dc.date.accessioned | 2018-05-15T08:21:38Z | |
dc.date.issued | 2018-06-26 | |
dc.identifier.issn | 1574-1702 | |
dc.identifier.issn | 1875-9076 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/11504 | |
dc.description.abstract |
Image posting and sharing on the social media clouds is a common activity of end users. During the uploading of an image, social media cloud automatically compresses the original image to reduce resolution and file size to save storage and provides service to speed up content loading in the web page. Image quality degradation on social media clouds decreases the user satisfaction level. Quality of Experience (QoE) experiment was conducted for assessing the end user’s satisfaction for image compression. During the experiment, four popular social media clouds were selected for 4 sample image hosting and images were captured with Canon DSLR and Samsung mobile cameras. The results show that Facebook and Twitter compress image less as compared to WeChat and Tumblr and user QoE ratings show that Facebook and Twitter compression level for the image is acceptable. Further, we found that decrease of luminance and chrominance has less impact on image quality compared to resolution scaling which has higher impact on quality. | |
dc.format.extent | 125-143 | |
dc.language.iso | en | |
dc.publisher | IOS Press | |
dc.relation.replaces | 10026.1/12000 | |
dc.relation.replaces | http://hdl.handle.net/10026.1/12000 | |
dc.title | Assessment of Quality of Experience (QoE) of Image Compression in Social Cloud Computing | |
dc.type | journal-article | |
dc.type | Article | |
plymouth.issue | 2 | |
plymouth.volume | 14 | |
plymouth.publication-status | Published | |
plymouth.journal | Multiagent and Grid Systems | |
dc.identifier.doi | 10.3233/MGS-180284 | |
pubs.merge-from | 10026.1/12000 | |
pubs.merge-from | http://hdl.handle.net/10026.1/12000 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dcterms.dateAccepted | 2018-02-25 | |
dc.identifier.eissn | 1875-9076 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.3233/MGS-180284 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2018-06-26 | |
rioxxterms.type | Journal Article/Review |